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from functools import partial
import torch
import torch.nn as nn
from timm.models.efficientnet import EfficientNet
from timm.models.efficientnet import decode_arch_def, round_channels, default_cfgs
from timm.models.layers.activations import Swish
from ._base import EncoderMixin
def get_efficientnet_kwargs(
channel_multiplier=1.0, depth_multiplier=1.0, drop_rate=0.2
):
"""Create EfficientNet model.
Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
Paper: https://arxiv.org/abs/1905.11946
EfficientNet params
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
'efficientnet-b0': (1.0, 1.0, 224, 0.2),
'efficientnet-b1': (1.0, 1.1, 240, 0.2),
'efficientnet-b2': (1.1, 1.2, 260, 0.3),
'efficientnet-b3': (1.2, 1.4, 300, 0.3),
'efficientnet-b4': (1.4, 1.8, 380, 0.4),
'efficientnet-b5': (1.6, 2.2, 456, 0.4),
'efficientnet-b6': (1.8, 2.6, 528, 0.5),
'efficientnet-b7': (2.0, 3.1, 600, 0.5),
'efficientnet-b8': (2.2, 3.6, 672, 0.5),
'efficientnet-l2': (4.3, 5.3, 800, 0.5),
Args:
channel_multiplier: multiplier to number of channels per layer
depth_multiplier: multiplier to number of repeats per stage
"""
arch_def = [
["ds_r1_k3_s1_e1_c16_se0.25"],
["ir_r2_k3_s2_e6_c24_se0.25"],
["ir_r2_k5_s2_e6_c40_se0.25"],
["ir_r3_k3_s2_e6_c80_se0.25"],
["ir_r3_k5_s1_e6_c112_se0.25"],
["ir_r4_k5_s2_e6_c192_se0.25"],
["ir_r1_k3_s1_e6_c320_se0.25"],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
act_layer=Swish,
drop_rate=drop_rate,
drop_path_rate=0.2,
)
return model_kwargs
def gen_efficientnet_lite_kwargs(
channel_multiplier=1.0, depth_multiplier=1.0, drop_rate=0.2
):
"""EfficientNet-Lite model.
Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite
Paper: https://arxiv.org/abs/1905.11946
EfficientNet params
name: (channel_multiplier, depth_multiplier, resolution, dropout_rate)
'efficientnet-lite0': (1.0, 1.0, 224, 0.2),
'efficientnet-lite1': (1.0, 1.1, 240, 0.2),
'efficientnet-lite2': (1.1, 1.2, 260, 0.3),
'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
'efficientnet-lite4': (1.4, 1.8, 300, 0.3),
Args:
channel_multiplier: multiplier to number of channels per layer
depth_multiplier: multiplier to number of repeats per stage
"""
arch_def = [
["ds_r1_k3_s1_e1_c16"],
["ir_r2_k3_s2_e6_c24"],
["ir_r2_k5_s2_e6_c40"],
["ir_r3_k3_s2_e6_c80"],
["ir_r3_k5_s1_e6_c112"],
["ir_r4_k5_s2_e6_c192"],
["ir_r1_k3_s1_e6_c320"],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True),
num_features=1280,
stem_size=32,
fix_stem=True,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
act_layer=nn.ReLU6,
drop_rate=drop_rate,
drop_path_rate=0.2,
)
return model_kwargs
class EfficientNetBaseEncoder(EfficientNet, EncoderMixin):
def __init__(self, stage_idxs, out_channels, depth=5, **kwargs):
super().__init__(**kwargs)
self._stage_idxs = stage_idxs
self._out_channels = out_channels
self._depth = depth
self._in_channels = 3
del self.classifier
def get_stages(self):
return [
nn.Identity(),
nn.Sequential(self.conv_stem, self.bn1, self.act1),
self.blocks[: self._stage_idxs[0]],
self.blocks[self._stage_idxs[0] : self._stage_idxs[1]],
self.blocks[self._stage_idxs[1] : self._stage_idxs[2]],
self.blocks[self._stage_idxs[2] :],
]
def forward(self, x):
stages = self.get_stages()
features = []
for i in range(self._depth + 1):
x = stages[i](x)
features.append(x)
return features
def load_state_dict(self, state_dict, **kwargs):
state_dict.pop("classifier.bias", None)
state_dict.pop("classifier.weight", None)
super().load_state_dict(state_dict, **kwargs)
class EfficientNetEncoder(EfficientNetBaseEncoder):
def __init__(
self,
stage_idxs,
out_channels,
depth=5,
channel_multiplier=1.0,
depth_multiplier=1.0,
drop_rate=0.2,
):
kwargs = get_efficientnet_kwargs(
channel_multiplier, depth_multiplier, drop_rate
)
super().__init__(stage_idxs, out_channels, depth, **kwargs)
class EfficientNetLiteEncoder(EfficientNetBaseEncoder):
def __init__(
self,
stage_idxs,
out_channels,
depth=5,
channel_multiplier=1.0,
depth_multiplier=1.0,
drop_rate=0.2,
):
kwargs = gen_efficientnet_lite_kwargs(
channel_multiplier, depth_multiplier, drop_rate
)
super().__init__(stage_idxs, out_channels, depth, **kwargs)
def prepare_settings(settings):
return {
"mean": settings["mean"],
"std": settings["std"],
"url": settings["url"],
"input_range": (0, 1),
"input_space": "RGB",
}
timm_efficientnet_encoders = {
"timm-efficientnet-b0": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b0"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b0_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b0_ns"]),
},
"params": {
"out_channels": (3, 32, 24, 40, 112, 320),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.0,
"depth_multiplier": 1.0,
"drop_rate": 0.2,
},
},
"timm-efficientnet-b1": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b1"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b1_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b1_ns"]),
},
"params": {
"out_channels": (3, 32, 24, 40, 112, 320),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.0,
"depth_multiplier": 1.1,
"drop_rate": 0.2,
},
},
"timm-efficientnet-b2": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b2"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b2_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b2_ns"]),
},
"params": {
"out_channels": (3, 32, 24, 48, 120, 352),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.1,
"depth_multiplier": 1.2,
"drop_rate": 0.3,
},
},
"timm-efficientnet-b3": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b3"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b3_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b3_ns"]),
},
"params": {
"out_channels": (3, 40, 32, 48, 136, 384),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.2,
"depth_multiplier": 1.4,
"drop_rate": 0.3,
},
},
"timm-efficientnet-b4": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b4"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b4_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b4_ns"]),
},
"params": {
"out_channels": (3, 48, 32, 56, 160, 448),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.4,
"depth_multiplier": 1.8,
"drop_rate": 0.4,
},
},
"timm-efficientnet-b5": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b5"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b5_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b5_ns"]),
},
"params": {
"out_channels": (3, 48, 40, 64, 176, 512),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.6,
"depth_multiplier": 2.2,
"drop_rate": 0.4,
},
},
"timm-efficientnet-b6": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b6"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b6_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b6_ns"]),
},
"params": {
"out_channels": (3, 56, 40, 72, 200, 576),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.8,
"depth_multiplier": 2.6,
"drop_rate": 0.5,
},
},
"timm-efficientnet-b7": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b7"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b7_ap"]),
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_b7_ns"]),
},
"params": {
"out_channels": (3, 64, 48, 80, 224, 640),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 2.0,
"depth_multiplier": 3.1,
"drop_rate": 0.5,
},
},
"timm-efficientnet-b8": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_b8"]),
"advprop": prepare_settings(default_cfgs["tf_efficientnet_b8_ap"]),
},
"params": {
"out_channels": (3, 72, 56, 88, 248, 704),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 2.2,
"depth_multiplier": 3.6,
"drop_rate": 0.5,
},
},
"timm-efficientnet-l2": {
"encoder": EfficientNetEncoder,
"pretrained_settings": {
"noisy-student": prepare_settings(default_cfgs["tf_efficientnet_l2_ns"]),
},
"params": {
"out_channels": (3, 136, 104, 176, 480, 1376),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 4.3,
"depth_multiplier": 5.3,
"drop_rate": 0.5,
},
},
"timm-tf_efficientnet_lite0": {
"encoder": EfficientNetLiteEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_lite0"]),
},
"params": {
"out_channels": (3, 32, 24, 40, 112, 320),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.0,
"depth_multiplier": 1.0,
"drop_rate": 0.2,
},
},
"timm-tf_efficientnet_lite1": {
"encoder": EfficientNetLiteEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_lite1"]),
},
"params": {
"out_channels": (3, 32, 24, 40, 112, 320),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.0,
"depth_multiplier": 1.1,
"drop_rate": 0.2,
},
},
"timm-tf_efficientnet_lite2": {
"encoder": EfficientNetLiteEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_lite2"]),
},
"params": {
"out_channels": (3, 32, 24, 48, 120, 352),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.1,
"depth_multiplier": 1.2,
"drop_rate": 0.3,
},
},
"timm-tf_efficientnet_lite3": {
"encoder": EfficientNetLiteEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_lite3"]),
},
"params": {
"out_channels": (3, 32, 32, 48, 136, 384),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.2,
"depth_multiplier": 1.4,
"drop_rate": 0.3,
},
},
"timm-tf_efficientnet_lite4": {
"encoder": EfficientNetLiteEncoder,
"pretrained_settings": {
"imagenet": prepare_settings(default_cfgs["tf_efficientnet_lite4"]),
},
"params": {
"out_channels": (3, 32, 32, 56, 160, 448),
"stage_idxs": (2, 3, 5),
"channel_multiplier": 1.4,
"depth_multiplier": 1.8,
"drop_rate": 0.4,
},
},
}
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